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Criminal Persons Recognition Using Improved Feature Extraction Based Local Phase Quantization

P. Karuppanan1,*, K. Dhanalakshmi2

1 Department of CSE, Pandian Saraswathi Yadav Engineering College, Sivaganga, 630561, Tamilnadu, India
2 Department of CSE, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

* Corresponding Author: P. Karuppanan. Email:

Intelligent Automation & Soft Computing 2022, 33(2), 1025-1043.


Facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. A major concern of facial recognition is achieving the accuracy on classification, precision, recall and F1-Score. Traditionally, numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification. But the simplified method is feature extraction by comparing the particular facial features of the images from the collected dataset namely Labeled faces in the wild (LFW) and Olivetti Research Laboratory (ORL) dataset. In this paper, the feature extraction is based on local phase quantization with directional graph features for an effective optimal path and the geometric features. Further, Person Identification based deep neural network (PI-DNN) has proposed are expected to provide a high recognition rate. Various performance metrics, such as recognition rate, classification accuracy, accuracy, precision, recall, F1-score is evaluated. The proposed method achieves high-performance values when it is compared with other existing methods. The novelty of this paper explains in understanding the various features of different types of classifiers used. It is mainly developed to recognize the human faces in the crowd, and it is also deployed for criminal identification.


Cite This Article

P. Karuppanan and K. Dhanalakshmi, "Criminal persons recognition using improved feature extraction based local phase quantization," Intelligent Automation & Soft Computing, vol. 33, no.2, pp. 1025–1043, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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